Moreover, we apply reinforcement learning to further improve the performance of the proposed hybrid GA. We apply the technique proposed by Zheng et al. ([1]}) that employs reinforcement learning to learn an adaptive Q-value to replace the \(\alpha \) -value in LKH to determine the candidate cities and evaluate the quality of the edges. And we further use the adaptive Q-value learned by Q-learning algorithm ([2]} [2]}) during the LKH local search process to replace the distance metric used in the process of generating offsprings in EAX-GA. In this way, both of LKH and EAX-GA can be enhanced by the reinforcement learning in our RHGA algorithm.
